Right now in my mapping, I am setting "include_in_all" to true, which means all the fields are included in _all field.
However, when I am searching, instead of wasting space, and putting everything in the _all field, I want to specify the specific fields to certain for (and taking into account the boost scores in the mapping).
How do I create a query that tells Elastic Search to only look at specific fields(not just 1) and take into account the boosting I gave it during my mapping?
Start with a multi_match query. It allows you to query multiple fields, giving them different weights, and it's usually the way to go when you have a search box.
{
"multi_match" : {
"query" : "this is a test",
"fields" : [ "subject^2", "message" ]
}
}
The query_string is more powerful but more dangerous too since it's parsed and can break. Use it only if you need it.
You don't need to keep data in _all field to query for a field.
You can use query_string or bool queries to search over multiple fields.
Related
I'm a novice to Elasticsearch (ES), messing around with the analyzers. As the documentation states, the analyzer can be specifed "index time" and "search time", depending on the use case.
My document has a text field title, and i have defined the following mapping that introduces a sub-field custom:
PUT index/_mapping
{
"properties": {
"title": {
"type": "text",
"fields": {
"custom": {
"type": "text",
"analyzer": "standard",
"search_analyzer":"keyword"
}
}
}
}
}
So if i have the text : "email-id is someid#someprovider.com", the standard-analyzer would analyze the text into the following tokens during indexing:
[email, id, is, someid, someprovider.com].
However whenever I try to query on the field (with different variations in query terms) title.custom, it results in no hits.
This is what I think is happening when i query with the keyword: email:
It gets analyzed by the keyword analyzer.
The field title.custom's value also analyzed by keyword analyzer (analysis on tokens), resulting in same set of tokens as mentioned earlier.
An exact match should happen on email token, returning the document.
Clearly this is not the case and there are gaps in my understanding.
I would like to know what exactly is happening during search.
On a generic level, I would like to know how the analysis and search happens when combination of search and index analyzer is specified.
search_analyzer is set to "keyword" for title.custom, making the whole string work as a single search keyword.
So, in order to get a match on title.custom, it is needed to search for "email-id is someid#someprovider.com", not a part of it.
search_analyzer is applied at search time to override the default behavior of the analyzer applied at indexing time.
Good question, but to make it simple let me explain one by one different use cases:
Analyzers plays a role based on
Type of query (match is analyzed while term is not analyzed query).
By default, if the query is analyzed like match query it uses the same analyzer on the search term used on a field that is used at index time.
If you override the default behavior by specifying the search_analyzer on a field that at query time that analyzer is used to create the tokens which will be matched with the tokens generated depends on the analyzer(Standard is default analyzer).
Now using the above three points and explain API you can figure out what is happening in your case.
Let me know if you need further information and would be happy to explain further.
Match vs term query difference and Analyze API to see the tokens will be helpful as well.
If I add a document with several fields to an Elasticsearch index, when I view it in Kibana, I get each time the same field twice. One of them will be called
some_field
and the other one will be called
some_field.keyword
Where does this behaviour come from and what is the difference between both of them?
PS: one of them is aggregatable (not sure what that means) and the other (without keyword) is not.
Update : A short answer would be that type: text is analyzed, meaning it is broken up into distinct words when stored, and allows for free-text searches on one or more words in the field. The .keyword field takes the same input and keeps as one large string, meaning it can be aggregated on, and you can use wildcard searches on it. Aggregatable means you can use it in aggregations in elasticsearch, which resembles a sql group by if you are familiar with that. In Kibana you would probably use the .keyword field with aggregations to count distinct values etc.
Please take a look on this article about text vs. keyword.
Briefly: since Elasticsearch 5.0 string type was replaced by text and keyword types. Since then when you do not specify explicit mapping, for simple document with string:
{
"some_field": "string value"
}
below dynamic mapping will be created:
{
"some_field": {
"type" "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
}
As a consequence, it will both be possible to perform full-text search on some_field, and keyword search and aggregations using the some_field.keyword field.
I hope this answers your question.
Look at this issue. There is some explanation of your question in it. Roughly speaking some_field is analyzed and can be used for fulltext search. On the other hand some_field.keyword is not analyzed and can be used in term queries or in aggregation.
I will try to answer your questions one by one.
Where does this behavior come from?
It is introduced in Elastic 5.0.
What is the difference between the two?
some_field is used for full text search and some_field.keyword is used for keyword searching.
Full text searching is used when we want to include individual tokens of a field's value to be included in search. For instance, if you are searching for all the hotel names that has "farm" in it, such as hay farm house, Windy harbour farm house etc.
Keyword searching is used when we want to include the whole value of the field in search and not individual tokens from the value. For eg, suppose you are indexing documents based on city field. Aggregating based on this field will have separate count for "new" and "york" instead of "new york" which is usually the expected behavior.
From Elastic 5.0 onwards, strings now will be mapped both as keyword and text by default.
I wanted to know how multi matching with _all work. Let's say I have the following query:
"multi_match": {
"query": x,
"type": "phrase",
"fields":"_all",
}
Does it search all available fields for the particular phrase and returns a record if the phrase exists in all fields? What if some of the fields have it and some other do not?
_all field is just field which concatenate all your fields into one big string and then analyze it in standard way - if no defined using standard analyzer for text. https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping-all-field.html
It's possible to remove some fields from _all fields while defining your mapping with param 'include_in_all' https://www.elastic.co/guide/en/elasticsearch/reference/current/include-in-all.html
So does it make sense to use phrase query on concatenation of your all fields? Rather not. I would say that multi_match can let you achieve similar goals as _all fields; you can search multiple fields in one query. But when using _all fields you can just use 'match' query.
_all field (which is removed in 6.0) index all the values from your json document whatever the field they appeared in.
I have an elasticsearch v2.x cluster with a "tags" index that contains about 5000 tags: {tagName, tagID}. Given a string, is it possible to query the tags index to get all tags that are found in that string? Not only do I want exact matches, but I also want to be able to control for fuzzy matches without being too generous. By too generous, a tag should only match if all tokens in the tag are found within a certain proximity of each other (say 5 words).
For example, given the string:
Model 22340 Sound Spectrum Analyzer
The following tags should match:
sound analyzer sound spectrum analyzer
BUT NOT
sound meter light spectrum chemical analyzer
I don't think it's possible to create an accurate elasticsearch query that will auto-tag a random string. That's basically a reverse query. The most accurate way to match a tag to a document is to construct a query for the tag, and then search the document. Obviously this would be terribly inefficient if you need to iterate over each tag to auto-tag a document.
To do a reverse query, you want to use the Elasticsearch Percolator API:
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-percolate.html
The API is very flexible and allows you to create fairly complex queries into documents with multiple fields.
The basic concept is this (assuming your tags have an app specific ID field):
For each tag, create a query for it, and register the query with the percolator (using the tag's ID field).
To auto-tag a string, pass your string (as a document) to the Percolator, which will match it against all registered queries.
Iterate over the matches. Each match includes the _id of the query. Use the _id to reference the tag.
This is also a good article to read: https://www.elastic.co/blog/percolator-redesign-blog-post
"query": {
"match": {
"tagName": {
"query": "Model 22340 Sound Spectrum Analyzer",
"fuzziness": "AUTO",
"operator": "or"
}
}
}
If you want an equal match so that "sound meter" will not match you will have to add another field for each tag containing the terms count in the tag name, add a script to count the terms in the query and add a comparison of the both in the match_query, see: Finding Multiple Exact Values.
Regarding the proximity issue: Since you require "Fuzzyness" you cannot control the proximity because the "match_phrase" query is not integrated with Fuzzyness, as stated by Elastic docs Fuzzy-match-query:
Fuzziness works only with the basic match and multi_match queries. It doesn’t work with phrase matching, common terms, or cross_fields matches.
so you need to decide: Fuzzyness vs. Proximity.
Of course you can. You can achieve what you want to get using only just match query with standard analyzer.
curl -XGET "http://localhost:9200/tags/_search?pretty" -d '{
"query": {
"match" : {
"tagName" : "Model 22340 Sound Spectrum Analyzer"
}
}
}'
I am using the snowball analyzer in my query string search ... like so
"query" : {
"query_string" : {
"query" : the-query-string-goes-here,
"default_operator" : "AND",
"analyzer" : "snowball"
}
}
this actually works but it does something weird ... searching for "fighting" will return results for "fight" but ignore results for "fighting". A search for "crews" will return results for "crew" but not "crews", also a search for "crew" also ignores results for "crews" ...
Anyone know what's going on?
Stemming makes sense when you apply it at both index time and query time. Now you are applying it at query time, so that you search for the stems of the words which are part of the query.
But I guess the index doesn't contain the stems since you haven't applied stemming at index time. You're actually searching on the _all field since you didn't specify any field name neither in your query nor using default_field (or fields) attribute supported by the query_string. The _all field is by default analyzed using the StandardAnalyzer.
There are different ways to solve this problem. I'd personally decide a set of fields on which you want to search in your query and apply to them stemming in your mapping. After that you don't need to specify the analyzer in your query since the configured analyzer for the field on which you are searching will be used.
Let me know if the answer is clear enough.
Thanks to #javanna for pointing me in the right direction. I solved this by setting the analyzer for the _all field to snowball. See this doc for details.
I'm using the Ruby tire gem, and I was able to specify the mapping in my model as follows:
mapping(_all: { analyzer: 'snowball' }) do
indexes :id, type: 'integer'
indexes :description
indexes :name, boost: 10
end
I formatted my query exactly like in the original question.